GA-ORB: A New Efficient Feature Extraction Algorithm for Multispectral Images Based on Geometric Algebra

Feature extraction, including feature detection and description, plays an important role in many computer vision applications. The feature extraction for multispectral images is especially challenging because the information is contained in both spectral and spatial spaces. Previous works, such as scale invariant feature transform (SIFT) algorithm based on geometric algebra (GA) theory (GA-SIFT) and speeded up robust features (SURF) algorithm based on GA theory (GA-SURF), have tried to extract features for multispectral images based on GA to simultaneously capture relations among multiple channels. However, those methods are hard to be applied in real-time tasks. Fortunately, oriented fast and rotated brief (ORB) is a feature extraction method faster than SIFT and SURF. But, the existing ORB algorithms are not capable of detecting features for multispectral images directly. In this paper, we propose a novel feature extraction method, geometric algebra based oriented fast and rotated brief (GA-ORB), for multispectral images based on the theory GA. First, the scale information in both spectral and spatial spaces of multispectral images is obtained in GA, where the inherent spectral structures can be retained successfully. Then, referring to the ORB, the images are computed in different scales and the interest points are detected and described in the GA space. The experimental results show that the GA-ORB outperforms some previous algorithms with respect to distinctiveness and robustness in extracting and matching interest points, and it can be computed much faster.